AI Innovation Architect – Knowledge Graphs & Ontology

Kinaxis Inc.•Remote,

6d•Hybrid

About The Position

The AI team is responsible for advancing machine learning solutions in the supply and demand space across industries such as Retail, Consumer Packaged Goods, and Life Sciences. Our work spans forecasting, optimization, replenishment, recommendation, explainability, and emerging AI techniques that help customers solve complex, real-world planning challenges. What makes this team unique is that we operate at the intersection of applied research, product innovation, and customer impact. We explore new methods, develop novel approaches, and turn them into practical capabilities that can shape the future of the Kinaxis platform. This is a team for people who want to work on meaningful problems, push the boundaries of applied AI in real business settings, and see their ideas influence products used by customers around the world.

Requirements

PhD in Computer Science, Artificial Intelligence, Knowledge Representation, or a related field.
Extensive experience modeling complex domains, with a track record of building enterprise ontologies and knowledge graph systems in production.
Strong hands-on experience building prototypes, proof-of-concepts, and early semantic systems that demonstrate the value of structured knowledge.
Deep expertise in ontology design and semantic modeling, including entities, relationships, constraints, and temporal or event-based modeling.
Experience defining ontology governance, versioning, and lifecycle management, and driving reuse and alignment across domains.
Ability to define long-term evolution strategies for the enterprise knowledge model and platform, balancing delivery with durable design.
Experience applying research and emerging techniques in knowledge representation, semantic systems, or AI, with a track record of translating new concepts into practical, product-oriented solutions.
Architect large-scale enterprise knowledge graphs that integrate structured, semi-structured, and unstructured data.
Hands-on experience with knowledge graph platforms and pipelines, including designing, ingestion, transformation, entity resolution, schema/ontology alignment, validation and performance at scale.
Drives the technical evaluation of emerging graph, semantic, and AI technologies with clear, defensible trade-off analysis
Understanding and familiarity of modern AI approaches such as agentic systems, LLMs, RAG, and explainable AI, with the ability to integrate structured knowledge effectively.
Demonstrated ability to identify, evaluate, and apply emerging research and technologies in knowledge representation, semantic systems, and AI, translating them into scalable architecture patterns and product capabilities that enable reuse, composability, and extensibility across products
Strong technical judgment and the ability to evaluate emerging semantic, graph, and AI technologies.
Demonstrated ability to influence technical direction across domains through expertise, credibility, and collaboration.
Strong programming ability (e.g., Python) and experience with modern data, graph, and cloud platforms.
Excellent communication skills, with the ability to bring clarity and alignment to complex concepts.

Nice To Haves

Deep experience with semantic technologies (e.g., RDF, OWL, SHACL, SPARQL)
Experience in supply chain, planning, or other complex operational domains
Background in temporal modeling, digital twins, or operational intelligence systems
Experience contributing to research, standards, or innovation in semantic systems or applied AI
Experience with enterprise SaaS environments and operating semantic/AI platforms at scale

Responsibilities

Own the enterprise knowledge model including entities, relationships, actions, constraints, and how they evolve over time.
Define and govern ontology standards, ensuring clear layering, reuse, and consistency across systems.
Provide technical leadership while remaining hands-on in semantic architecture and knowledge graph development.
Design and evolve enterprise knowledge graph platforms that integrate structured, semi-structured, and unstructured data, enabling reasoning, inference, and contextual retrieval.
Design the underlying data and graph architecture, including ingestion pipelines, transformation and mapping, entity resolution, schema alignment, validation, and both batch and streaming updates.
Guide key technical decisions across ontology design, graph architecture, and AI integration, including trade-offs between materialized inference, constraint validation, and query-time reasoning at scale.
Partner closely with product and engineering to ensure models are practical, scalable, and aligned to real-world use cases.
Mentor others and help build a culture of structured thinking, semantic clarity and innovation.